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Optimized utilization of interline power flow controller in an integrated power system

WORLD JOURNAL OF ENGINEERING 2020年第2期17卷 261-266页

作者： Mishra, Akanksha Kumar, G. V. Nagesh Bali, Sravana Kumar Vignans Inst Engn Women Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India JNTUA Coll Engn Dept EEE Pulivendula India GITAM Univ Dept EEE Visakhapatnam Andhra Pradesh India

Purpose There is a worldwide need to amplify the usage of renewable energy in the manufacture of electrical energy. Thus, the integrated energy systems (IESs) have become a major part of today's power systems. Wind and solar energies are intermittent power sources and may lead to voltage and power flow instabilities. The purpose of this paper is to use the interline power flow controller (IPFC) for limiting the overloading of the transmission lines and improving the voltage stability of the IES. Design/methodology/approach This paper deals with an integrated system consisting of wind and solar energies and conventional systems. An appropriate position for the IPFC in the IES is proposed based on the disparity line utilization factor. The IPFC is then tuned for decreasing the loss of power and lessening the voltage deviation using the grey wolf algorithm. Findings The method is implemented on a modified IEEE 30-bus system. Results from the study show that the mega volt ampere (MVA) loading of the overloaded lines is reduced for the IES. Also, the voltage stability and the voltage profile of the system are improved to a major extent. The real and reactive power loss of the system is also brought down. Originality/value The use of renewable energy sources is a need of the present world to overcome environmental problems. This research focuses on the use of flexible AC transmission system (FACTS) devices with renewable sources incorporated in the power system. Very limited research has been done in this field. The IPFC, which is one of the most advanced FACTS device, is used for the study.

关键词： Integrated power system Interline power flow controller Optimization grey wolf algorithm Integrated energy sources

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Incremental Learning Method for Robot Error Based on GWO-XGBoost algorithm 36

Incremental Learning Method for Robot Error Based on GWO-XGB...

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36th Chinese Control and Decision Conference (CCDC)

作者： Lu, Zi-Wei Liao, Zhao-Yang Wu, Jin-Zhu Li, Chuang-Chuang Ou, Yang-Min Xu, Zhi-Hao Northeastern Univ China Software Coll Shenyang Peoples R China Guangdong Acad Sci Guangdong Key Lab Modern Control Teclmol Inst Intelligent Mfg Guangzhou Peoples R China

ISBN: (纸本)9798350387780;9798350387797

In this paper, a method based on the GWO-XGBoost incremental learning model is proposed to improve the robot's positioning accuracy. It is crucial to reduce the positioning error of the robot. In addition, the trajectory and position of the robot are always changing during the working process. Therefore, to address this problem, inspired by the idea of online learning, the XGBoost incremental learning model optimized with improved grey wolf algorithm is used to predict the positioning error of industrial robots. Firstly, the grey wolf algorithm is used to determine the optimal hyper-parameters of XGBoost model, then XGBoost is used for modelling prediction, and finally the accuracy and generalization ability is improved by online incremental learning. The GWO-XGBoost model has good fitting and prediction ability, the model is validated on Universal robots by online incremental training with good results.

关键词： Industrial robots Error compensation Incremental learning XGBoost grey wolf algorithm

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Data Anomaly Detection in Power System State Estimation Based on Support Vector Machine 5

Data Anomaly Detection in Power System State Estimation Base...

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5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM)

作者： Guo, Jiahui Nie, Bingqing Shandong Univ Sch Elect Engn Jinan Peoples R China Shandong Univ Sch Foreign Languages & Literature Jinan Peoples R China

ISBN: (纸本)9798350363272;9798350363265

To address the significant impact of anomalous data on the performance of power system state estimation, this study proposes a Forecasting-Aided state estimation (FASE) anomaly detection method for power systems based on support vector machines (SVM). Initially, to tackle the issue of poor prediction accuracy in traditional FASE, the method utilizes SVM, leveraging predictive data, measurement data, and estimated values to detect various types of data anomalies, including bad data, load spikes, and single-phase earth faults. Additionally, considering the influence of penalty factors and kernel function parameters on classification accuracy, a grey wolf optimization is employed to optimize SVM parameters, enhancing the accuracy of data anomaly detection while balancing computational speed. Finally, simulation tests on the IEEE 33-bus system demonstrate that the proposed method offers strong universality and real-time capabilities, effectively detecting various types of data anomalies in power systems and improving the performance of state estimation.

关键词： forecasting-aided state estimation anomaly detection support vector machine grey wolf algorithm

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Multi-Objective Electric Truck Path Optimization Model with Time Window 4

Multi-Objective Electric Truck Path Optimization Model with ...

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4th International Conference on Control, Robotics and Cybernetics (CRC)

作者： Qi Yuanhao Huang Yinuo Ye Yuanyuan Wang Wanli Fu Yaping Wang Kai Qingdao Univ Sch Elect Engn Qingdao Peoples R China Qingdao Publ Secur Bur Qingdao Peoples R China Qingdao Univ Business Sch Qingdao Peoples R China

ISBN: (数字)9781728146201

ISBN: (纸本)9781728146201

Aiming at the problem of electric truck path, an electric truck path optimization model with time window is proposed. The model considers the relationship between the speed of the electric truck, the load of the cargo, and the energy consumption, minimizing the total power consumption of the distribution center and the total advance or delay time. At the same time, simulation experiment is carried out by using the grey wolf algorithm. The comparison between the simulation results and the genetic algorithm shows that the grey wolf algorithm can solve the vehicle path planning with time window easily and effectively. It has better convergence and accuracy and is an effective way to solve such path optimization problems.

关键词： power consumption EVRPTW grey wolf algorithm

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Application of DE-GWO-SVM algorithm in Business Order Prediction Model 11

Application of DE-GWO-SVM Algorithm in Business Order Predic...

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11th IEEE International Conference on Software Engineering and Service Science (IEEE ICSESS)

作者： Liu, Jing Zhu, Xiaojun Zhang, Yanfeng Minist Publ Secur Res Inst 3 Dept Cyber Secur Beijing Peoples R China Govt Affairs Serv Adm Xicheng Beijing Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou Peoples R China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165790

To judge whether customers will choose the time deposit business launched by the bank is a problem of prediction classification, and it is also a problem that bankers want to know and urgently need to solve. This paper constructs a prediction model of banking business order based on DE-GWO-SVM *** model uses Differential Evolution algorithm and grey wolf algorithm to optimize the Support Vector Machine, and then applies the model to the prediction of bank marketing business order, which can effectively classify and identify whether bank customers will buy the time deposit business. Finally through the model experiment and comparing the grey wolf algorithm for without optimization, it is concluded that the model of banking business order based on DE-GWO-SVM algorithm, at an effective prediction accuracy of 96.8%, will help Banks identify the target customer groups, carry on the accurate marketing, improve the success rate of business marketing.

关键词： component Differential Evolution algorithm grey wolf algorithm Support Vector Machine business order prediction classification

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Research on improved active disturbance rejection control of continuous rotary motor electro-hydraulic servo system

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Journal of Central South University 2020年第12期27卷 3733-3743页

作者： WANG Xiao-jing FENG Ya-ming SUN Yu-wei Key Laboratory of Advanced Manufacturing and Intelligent Technology(Ministry of Education) Harbin University of Science and TechnologyHarbin 150080China

In order to meet the precision requirements and tracking performance of the continuous rotary motor electro-hydraulic servo system under unknown strong non-linear and uncertain strong disturbance factors,such as dynamic uncertainty and parameter perturbation,an improved active disturbance rejection control(ADRC)strategy was *** state space model of the fifth order closed-loop system was established based on the principle of valve-controlled hydraulic *** the three parts of ADRC were improved by parameter perturbation and external disturbance;the fast tracking differentiator was introduced into linear and non-linear combinations;the nonlinear state error feedback was proposed using synovial control;the extended state observer was determined by nonlinear *** addition,the grey wolf algorithm was used to set the parameters of the three *** simulation and experimental results show that the improved ADRC can realize the system frequency 12 Hz when the tracking accuracy and response speed meet the requirements of double ten indexes,which lay foundation for the motor application.

关键词： continuous rotary electro-hydraulic servo motor active disturbance rejection control(ADRC) fast tracking differentiator(TD) non-linear state error feedback(NLSEF) extended state observer(ESO) grey wolf algorithm

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Separation Method of Main and Foreign Water for the Measuring Weirs of Danjiangkou Earth-Rock Dam

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WATER 2022年第22期14卷 3620-3620页

作者： Fang, Weihua Zhang, Weiping Zhang, Chenghan Xie, Zhiwen Yu, Tiantang Minist Water Resources Nanjing Res Inst Hydrol & Water Conservat Automat Nanjing 210012 Peoples R China Hanjiang Grp Corp Ltd Wuhan 430048 Peoples R China Hanjiang Grp Corp Ltd Danjiangkou Hydroelect Power Plant Danjiangkou 442700 Peoples R China Hohai Univ Dept Engn Mech Nanjing 211100 Peoples R China

Reservoir water and rainfall are the two main factors affecting the seepage of earth-rock dams, but the seepage measurement includes the seepage discharge caused by the reservoir water and rainfall. Only by obtaining the seepage discharge caused by the reservoir water can we evaluate the seepage safety state. In this paper, a statistical model for seepage monitoring of earth-rock dams is established. Normal distribution function and Rayleigh distribution function are used as the lag functions of reservoir water and rainfall, respectively. The grey wolf algorithm is used to solve the lag days, and the partial least square method is adopted to solve the regression coefficient of the statistical model. Then, the reservoir water (main water) and rainfall infiltration (foreign water) parts of the measuring weir are separated with the statistical model. The developed method is used to separate the main and foreign water parts of the three measuring weirs of the Danjiangkou earth-rock dam. The results show that the overall accuracy of the models is high (the multi-correlation coefficients are about 0.95), and the separated main and foreign water seepage discharge conforms to the seepage law of earth-rock dam, which verifies the effectiveness of the method.

关键词： seepage statistical model lag function partial least square method grey wolf algorithm measuring weir reservoir water rainfall infiltration Danjiangkou earth-rock dam

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Prediction of bond strength of reinforced concrete structures based on feature selection and GWO-SVR model

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CONSTRUCTION AND BUILDING MATERIALS 2023年第1期400卷

作者： Fan, Congcong Zheng, Yuanxun Wang, Shaoqiang Ma, Junjie Zhengzhou Univ Sch Water Conservancy & Civil Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China

Bond strength, as a mechanical property of reinforced concrete (RC) structures, is a crucial factor affecting the force characteristics of RC structures. In order to assess the load-bearing and deformation capacity of RC structures, it is essential to develop a model that can accurately predict the bond strength of RC structures. Therefore, a prediction model based on Random Forest (RF) feature selection and grey wolf algorithm optimized (GWO) support vector regression (GWO-SVR) is synthesized in this paper. An extensive database containing 1008 bonded slip test samples was first collected, and various feature parameters were finally preprocessed to filter 935 sets of test data. Next, the optimized support vector machine was trained using the training set data using the GWO-optimized SVR method. Meanwhile, the prediction accuracy of the model was evaluated using the integrated absolute error (IAE), the coefficient of determination (R2), and the mean absolute error (MAE). The model with a good prediction effect can predict the bond strength of unknown tests. When the sample size is small and the number of features is extensive, redundant features must be filtered out before the model prediction. The model prediction after feature selection is significantly better than that before training. The results show that the GWO-SVR model proposed in this study has a higher prediction accuracy than the existing bond strength model. The prediction results of the GWO-SVR model (Test set: R2 = 0.9547, MAE = 1.2487 MPa, IAE = 12.761%) outperform benchmark models such as the multilayer perceptron (MLP) and linear regression (LR). According to the training accuracy of the regression model, it can be found that the GWO-SVR model has good generalization performance (Training set: R2 = 0.9562, MAE = 1.1203 MPa, IAE = 12.182%). In addition, different optimization-seeking algorithms, kernel function types, and machine learning models have significant effects on the prediction results of

关键词： Reinforced concrete structure Bond tests Random forest feature selection grey wolf algorithm Support vector regression prediction

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A time power-based grey model with Caputo fractional derivative and its application to the prediction of renewable energy consumption

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CHAOS SOLITONS & FRACTALS 2022年第0期164卷

作者： Zhang, Yonghong Li, Shouwei Li, Jingwei Tang, Xiaoyu Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Sch Econ & Management Nanjing 211189 Peoples R China Southeast Univ Res & Dev Ctr Syst & Informat Engn Nanjing 211189 Peoples R China

The development of renewable energy is related to the sustainable development of social economy and environmental protection. A time power-based grey model with Caputo fractional derivative of renewable energy consumption is established in this study by introducing Caputo fractional derivative and fractional cumulative generator into the time power-based grey model. The analytical solution of this novel model is obtained using Laplace transform, and parameters of the model are optimized using grey wolf optimization algorithm. The model is then verified by using the renewable energy consumption of China, America, and Germany as examples. Results showed that fitting and testing effect of the model is better than that of other models. Finally, the proposed model is used to predict the future development trend of renewable energy consumption in these three countries.

关键词： Caputo fractional derivative Factional accumulation generation Time power grey model Laplace transform grey wolf algorithm

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High precision state of health estimation of lithium-ion batteries based on strong correlation aging feature extraction and improved hybrid kernel function least squares support vector regression machine model

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JOURNAL OF ENERGY STORAGE 2024年 90卷

作者： Feng, Renjun Wang, Shunli Yu, Chunmei Hai, Nan Fernandez, Carlos Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland

The state of health (SOH) of lithium-ion batteries plays a crucial role in maintaining the stability of electric vehicle systems. To address the issue of low accuracy in existing prediction models, this article introduces an enhanced grey wolf algorithm for optimizing the hybrid kernel least squares support vector regression machine used in lithium-ion battery SOH prediction. This research extracted four key health features from the raw data of each battery in the Cycle dataset, which is publicly accessible. Data preprocessing of health features involved Pearson correlation analysis and Hampel filtering techniques. The framework of least squares support vector regression constructs a hybrid kernel function of polynomial kernel function and radial basis function. The integration of differential evolution and the law of survival of the fittest into the grey wolf algorithm enhances its optimization ability. The improved grey wolf algorithm optimizes the parameters of the hybrid kernel least squares support vector regression machine, improving the accuracy and robustness of the model. After data validation, it is known that the optimal average absolute error value predicted by the model can reach 0.32 %. This indicates that the proposed method is effective and feasible.

关键词： Lithium -ion battery State of health Hybrid kernel function grey wolf algorithm Least squares support vector regression machine

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